Admission requirements
Assumed Prior Knowledge
The below list indicates assumed prior knowledge for this course, and undergraduate courses where you may have obtained it:
Working knowledge of Python (Introduction to Programming)
Linear algebra (Linear Algebra for Computer Scientists 1 & 2)
Calculus (Calculus 1 & 2)
Basic knowledge of machine learning (Machine Learning)
Description
The aim of this unit is to give you an introduction to computer vision: the theory, principles, techniques, algorithms and applications. The course covers topics from early to mid-level vision, i.e. the analysis and enhancement of images/videos, and high-level vision facilitating the understanding of the content of images/videos. Key algorithms will be covered ranging from classical (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. (CNNs, GANs). Application areas of computer vision are far-reaching and wide, from data compression to measuring the quality of performing actions by humans. The techniques in image processing and computer vision may be used in autonomous driving, medical imaging, CGI, remote sensing, pedestrian behaviour analysis, facial recognition and regeneration, traffic analysis, biometrics, product quality assurance, and much more.
Course objectives
Upon successful completion of the course students will be able to:
Understand low-level image processing methods such as filtering and edge detection
Understand the principles of different deep neural networks techniques and their applications to visual data
Understand core computer vision tasks for instance, matching, segmentation, detection, tracking and generation
Understand the ethical and privacy-related implications of large datasets and models.
Apply mathematical techniques learnt in prior courses (including linear algebra and calculus) to solve problems in computer vision.
Implement common existing models for computer vision tasks
Analyze the advantages and disadvantages of different computer vision algorithms
Relate and compare the possible techniques to solve different computer vision problems
Timetable
The most updated version of the timetables can be found on the students' website:
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudymap will automatically be displayed in MyTimetable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Pleas note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of instruction
Weekly lectures
Practical session
Assignments
Assessment method
Written exam (60%)
Practical assignments (40%)
- Two assignments of 10%
- One assignment of 20%
The grade for the written exam should be 5.5 or higher in order to complete the course. The average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0. Each assignment has a re-sit opportunity (a later submission). The maximum grade for a re-sit assignment is 6.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Slides contain all the necessary material for the course. For a few lectures, additional material will be made available through the course webpage.
The following books are recommended but not mandatory for the course:
Foundations of Computer Vision, Antonio Torralba, Phillip Isola and William T. Freeman.
Computer Vision: Algorithms and Applications, 2nd edition draft, Richard Szaliski (available for free online)
Registration
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudymap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information. An exemption is the fall semester for 1st year bachelor students, the student administration will enroll this group.
Please note that it is compulsory to register for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course.
Extensive FAQ on MyStudymap can be found here.